This repository contains the code of the paper Trustworthy Super-Resolution of Multispectral Sentinel-2 Imagery with Latent Diffusion.
Run LDSR-S2 directly in Google Colab! These notebooks let you fetch Sentinel-2 imagery, apply super-resolution, and save results — with or without writing code.
| Notebook Name | Description | Link |
|---|---|---|
| LDSR-S2 No-Code | 🔘 No coding required — chose point on a map and download SR results as GeoTIFFs |  |
| LDSR-S2 Walkthrough | 🧪 Code-level walkthrough with uncertainty estimation and advanced plotting | |
| LDSR-S2 & SEN2SR | 🔄 Use LDSR-S2 alongside SEN2SR to compare results on 10m + 20m bands | |
🧪 Status: LDSR-S2 has exited the experimental phase as of v1.0.0
📌 For super-resolving 20m bands, check out SEN2SR, or use it alongside LDSR-S2 in the third notebook.
If you use this model in your work, please cite
@ARTICLE{ldsrs2,
author={Donike, Simon and Aybar, Cesar and Gómez-Chova, Luis and Kalaitzis, Freddie},
journal={IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
title={Trustworthy Super-Resolution of Multispectral Sentinel-2 Imagery With Latent Diffusion},
year={2025},
volume={18},
number={},
pages={6940-6952},
doi={10.1109/JSTARS.2025.3542220}}pip install opensr-modelMinimal Example
import opensr_model # import pachage
model = opensr_model.SRLatentDiffusion(config, device=device) # create model
model.load_pretrained(config.ckpt_version) # load checkpoint
sr = model.forward(torch.rand(1,4,128,128), custom_steps=100) # run SRRun the 'demo.py' file to gain an understanding how the package works. It will SR and example tensor and save the according uncertainty map.
Output of demo.py file:
The model should load automatically with the model.load_pretrained command. Alternatively, the checkpoints can be found on HuggingFace
This package contains the latent-diffusion model to super-resolute 10 and 20m bands of Sentinel-2. This repository contains the bare model. It can be embedded in the "opensr-utils" package in order to be applied to Sentinel-2 Imagery.
Example on real S2 image
Examples on S2NAIP training dataset
This repository has left the experimental stage with the publication of v1.0.0.
